首页> 外文期刊>Network >Adaptation In Multisensory Neurons: Impact On Cross-modal Enhancement
【24h】

Adaptation In Multisensory Neurons: Impact On Cross-modal Enhancement

机译:多感觉神经元的适应:对交叉模式增强的影响。

获取原文
获取原文并翻译 | 示例
       

摘要

Adaptation is a ubiquitous property of sensory neurons. Multisensory neurons, receiving convergent input from different sensory modalities, also likely exhibit adaptation. The responses of multisensory superior colliculus neurons have been extensively studied, but the impact of adaptation on these responses has not been examined. Multisensory neurons in the superior colliculus exhibit cross-modal enhancement, an often non-linear and non-additive increase in response when a stimulus in one modality is paired with a stimulus in a different modality. We examine the possible impact of adaptation on cross-modal enhancement within the framework of a simple model of adaptation for a neuron employing a saturating, logistic response function. We consider how adaptation to an input's mean and standard deviation affects cross-modal enhancement, and also how the statistical correlations between two different modalities influence cross-modal enhancement. We determine the optimal bimodal stimuli to present a bifnodal neuron that evoke the largest changes in cross-modal enhancement under adaptation to input statistics. The model requires separate gains for each modality, unless the statistics specific to each modality have been standardised by prior adaptation in earlier, unisensory neurons. The model also predicts that increasing the correlation coefficient between two modalities reduces a multisensory neuron's overall gain.
机译:适应是感觉神经元的普遍特性。接收来自不同感觉模态的会聚输入的多感觉神经元也可能表现出适应性。已经广泛研究了多感觉上丘神经元的反应,但尚未研究适应对这些反应的影响。上丘中的多感觉神经元表现出交叉模态增强,当一种模态的刺激与另一种模态的刺激配对时,通常是非线性和非累加的响应增加。我们在采用饱和,逻辑响应函数的神经元的简单适应模型框架内,研究了适应对交叉模式增强的可能影响。我们考虑对输入的均值和标准差的适应如何影响交叉模态增强,以及两个不同模态之间的统计相关性如何影响交叉模态增强。我们确定最佳的双峰刺激,以呈现一个双端神经元,该双元神经元在适应输入统计数据的情况下引起跨峰增强的最大变化。该模型需要为每个模态分别分配收益,除非已通过在较早的单感觉神经元中进行事先适应对每个模态的统计数据进行了标准化。该模型还预测,增加两种模态之间的相关系数会降低多感觉神经元的整体增益。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号